Episode
The AI Productivity Paradox: Why 10X Output Doesn't Mean 10X Business Outcome
- Podcast
- Tech Lead Journal
- Published
- Jun 29, 2026
- Duration seconds
- 3939
- Processing state
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Summary
What if optimizing for AI output is actually slowing your company down? When code becomes nearly free to produce, the organizations still measuring productivity by output are solving the wrong problem. In this episode, Mik Kersten, author of “Project to Product” and the forthcoming “Output to Outcome,” shares why the real challenge of the AI era isn’t generating more code — it’s building organizations that can turn that output into customer and business value. Drawing on Carlota Perez’s model of technological revolutions and the theory of constraints, Mik explains how AI has removed the output bottleneck that software organizations were built around, and where the new constraints now live. He introduces three core models from the book — the outcome loop, the product operating model, and the outcome tree — as a framework for adapting how organizations plan, fund, and deliver value. Mik also addresses one of the most pressing decisions leaders face today: whether to cut headcount based on AI productivity gains, and why doing so without outcome visibility is a dangerous bet. The conversation covers how organizational structure, decision-making accountability, and leadership roles all need to shift — not just development practices. Timestamps: (00:00:00) Trailer & Intro (00:02:40) What Makes Output to Outcome Different From Project to Product? (00:05:02) Why Did Mik Write Every Word of This Book Without AI? (00:08:18) How Do the AI Prompts at the End of Each Chapter Work? (00:11:53) What Happens to Organizations When AI Makes Software Output 10 to 100 Times Cheaper? (00:15:03) How Do Past Technological Revolutions Help Us Understand the AI Era? (00:19:25) Is the Traditional Software Developer Role Gone for Good? (00:23:30) Why Do Some Companies Experience an AI Product…